VDBench : A Benchmarking Toolkit for Thin- client based Virtual - - PowerPoint PPT Presentation

VDBench: A Benchmarking Toolkit for Thin- client based Virtual Desktop Environments

Alex Berryman, berryman@oar.net In collaboration with: Dr. Prasad Calyam (OSC/OARnet), Prof. Albert Lai (OSUMC), Matt Honigford (VMware) IEEE CloudCom, Indianapolis, IN December 2nd, 2010

Topics of Discussion

• Background and Motivation
• VDBench Components and Data Flows

– Architecture, Metrics, Techniques

• VDBench Experiment Results

– User Load Simulation based Benchmarking – Slow-motion Application Interaction based Benchmarking

• VDBench Use Cases

– Performance Mapping to User Application and Group Profiles – Resource Location Selection for Performance Balancing

2

Virtual Desktop Infrastructure

• VMware View is one of the popularly used VDI solutions

– Personalized user desktops, applications and data access while maintaining centralized control and security

3

Research Context

• Recent advances in thin clients and the numerous benefits in

transitioning user desktops to cloud environments

– Convenience, Cost savings, Green IT, Security, …

• Need for “system-aware”, “network-aware”, “human-aware”

frameworks and tools to deploy virtual desktop clouds

– Existing work focuses mainly upon system (i.e., CPU and memory) measurements for server-side resource adaptation – Our focus is to couple client-and-server resource adaptation with measurements of network health and user experience

• Minimize cloud resource over-commitment
• Avoid guesswork in configuring thin client protocols
• Deliver optimum user experience of virtual applications

4

VDBench Components and Data Flows

5

Application Tasks Progression

6

User-Load Simulation Metrics

• Controlled Variables:

– Number of VMs concurrently running to simulate user-load – “Ceiling Response Time” is set to 30% increase from the ideal “Application Response Time”

• Metrics:

– Application Response Time

• Application Open Time
• Aggregate Inter-application task

– Matlab surface visualization

• Atomic tasks

– Alt+tab, ‘Save As’, web-page loads

– Memory Availability

• (Memory Allocated - Memory Balloon) / Memory Allocated

– Available Memory utilization

• Memory Used / (Memory Allocated - Memory Balloon)

7

Slow-Motion Benchmarking Metrics

• Controlled Variables:

– Network Health:

• Latency, Loss, Available Bandwidth

– Codec choice:

• RDP, RGS, PCoIP
• Metrics:

– Render Time: time taken for a screen update to complete

• Assume that the time between the first and last packet of a screen

update is equal to the time taken to display the update

– Coding Efficiency: amount of data to transfer screen information

• Atomic: Single screen update
• Aggregate: Many screen updates (e.g., Video playback)

– Video Quality: ratio of actual video playback with ideal playback

• Measures the information loss when a codec transmits a video file

8

Slow-Motion Benchmarking Technique

9

VDBench Control Logic

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Research Goals and Contributions

• Develop VDBench techniques to simulate realistic user workflows

under synthetic system loads and network health impairments

– Useful to measure corresponding user-perceived ‘interactive response times’ (e.g., application launch time, web-page download time, video quality)

• Correlate thin-client user events with server-side resource

performance events and develop novel performance metrics

– Proposed the use of ‘marker packets’ that leverage and extend earlier research on slow-motion benchmarking of thin-client performance

• Generate resource utilization profiles of different applications and

user groups based on VDBench measurements

– Useful to intelligently map pools of desktops to resources such that user satisfaction is ensured with minimal resource over-provisioning

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Related Work

• Slow-motion benchmarking of thin-client display protocols was

developed by [Nieh, et. al.]

– Focus is on measuring user perceived performance by monitoring client-side of a remote desktop session – Lai, et. al. used slow-motion benchmarking to investigate thin-client display protocol characteristics over WAN connections

• Existing virtual desktop benchmarking tools such as “Login VSI”

developed by [Sprujit, et. al.] simulate realistic user workflows

– They neglect the distinction between client-side rendering and server- side processing and hence cannot measure thin-client user experience

• VNCPlay [Zeldovich et. al.] and Deskbench [Rhee et. al.] focus on

thin-client workflow replay

– They do not address event-synchronization with server side performance measurements

12

VDBench Experiments Overview

• VDI Scalability

– Stress test VMs under various user application work loads

• User Applications: MS Excel, IE Browser, Windows Media Player,

Matlab

– We used “Autoit” user application work loads (scripts for repeatable

and automated GUI interactions with key presses, mouse movements)

• VDI Reliability

– Evaluate performance of remote desktop protocols

• Protocols Evaluated: Microsoft Remote Desktop Protocol (RDP), HP

Remote Graphics Software (RGS), Teradici PC-over-IP (PCoIP)

– We used Netem for network (i.e., bandwidth, delay, loss) emulation

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User Load Simulation based Benchmarking

• Memory utilization shows a linear

increase in Memory Balloon size as the number of VMs increase

• Increasing Memory Balloon size
• n each VM leads to an increase
• f user-perceived application
• pen and task times

14

Slow-motion Application based Benchmarking

• Slow-motion benchmarking

results give the bandwidth consumption of each protocol for a specific type of screen content and network health condition

15

VDBench Use Cases

• Performance Mapping to User Application and Group Profiles
• Resource Location Selection for Performance Balancing

16

Thank you for your attention!